Fingerprint enrollment method and apparatus

ABSTRACT

A processor fingerprint enrollment method and apparatus is disclosed. A processor implemented fingerprint enrollment method includes performing a matching between a received input fingerprint image of a user and one or more enrolled fingerprint images, and selectively, based on a result of the matching identifying a matched enrolled fingerprint image from the one or more enrolled fingerprint image and based on a calculated degree of diversity in the fingerprint corresponding to an overlapping region between the input fingerprint image and the matched enrolled fingerprint image, storing the input fingerprint as another enrolled fingerprint image.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent Ser. No. 15/629,324filed on Jun. 21, 2017, which claims the benefit under 35 USC § 119(a)of Korean Patent Application No. 10-2016-0147401 filed on Nov. 7, 2016,in the Korean Intellectual Property Office, the entire disclosures ofwhich are incorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to a fingerprint enrollment method andapparatus.

2. Description of Related Art

Biometrics-based authentication technology may be used to authenticate auser, for example, a fingerprint, an iris, a voice, a face, and bloodvessels. Such biological characteristics used for such userauthentication differ from individual to individual, rarely changeduring a lifetime, and have a low risk of being stolen or copied. Inaddition, individuals do not need to intentionally carry suchcharacteristics at all times, and thus may not suffer an inconvenienceusing the biological characteristics.

Currently, fingerprint recognition approaches are most commonly used dueto their high level of convenience, security, and economic efficiency. Afingerprint recognition approach may reinforce security of a user deviceand enable a user to receive various application services, for example,mobile payment, more readily.

User authentication using a fingerprint may start with enrolling afingerprint image to be used for fingerprint verification and storingthe enrolled fingerprint image. Subsequently, when a fingerprint imagenewly received from a user requesting the authentication corresponds tothe enrolled fingerprint image, the user may be authenticated as anenrolled user.

Recently, as portable devices have become smaller in size, the size ofthe fingerprint sensing region included in such a portable device havealso decreased.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is the Summaryintended to be used as an aid in determining the scope of the claimedsubject matter.

In one general aspect, a processor implemented fingerprint enrollmentmethod includes performing a matching between a received inputfingerprint image of a user and one or more enrolled fingerprint images,selectively, based on a result of the matching identifying a matchedenrolled fingerprint image from the one or more enrolled fingerprintimage and based on a calculated degree of diversity in the fingerprintcorresponding to an overlapping region between the input fingerprintimage and the matched enrolled fingerprint image, storing the inputfingerprint as another enrolled fingerprint image.

The selective storing of the input fingerprint may further includeextracting, in response to the matching identifying the matched enrolledfingerprint, the overlapping region as between the input fingerprintimage and the matched enrolled fingerprint image, calculating the degreeof diversity in the fingerprint corresponding to the extractedoverlapping region, and selectively storing the input fingerprint imageas the other enrolled fingerprint image based on the calculated degreeof diversity.

The selective storing of the input fingerprint image may includeselecting, dependent on the calculated degree of diversity, between afirst of storing the input fingerprint image as the other enrolledfingerprint image and a second of requesting the user to input anotherfinger fingerprint image and not storing the input fingerprint image asthe other enrolled fingerprint image.

The selective storing of the input fingerprint image may furtherinclude, dependent on a determined total number of stored enrolledfingerprint images and/or a calculated effective area size correspondingthe stored enrolled fingerprint images, not requesting the user to inputthe other fingerprint image and ceasing an enablement process of theenablement method.

The method may further include calculating the degree of diversity inthe fingerprint corresponding to the extracted overlapping region,including calculating a similarity score between the input fingerprintimage and the matched enrolled fingerprint image based on a calculatedsimilarity corresponding to the overlapping region, wherein, arelationship between the similarity score and the degree of diversitymay exist such that, in response to an increase in the similarity score,the degree of diversity decreases, and in response to a decrease in thesimilarity score, the degree of diversity increases.

The calculating of the similarity score may include calculating a sharedimage similarity between a first shared image portion of the inputfingerprint image corresponding to the overlapping region and a secondshared image portion of the matched enrolled fingerprint imagecorresponding to the overlapping region, and calculating the similarityscore based on the calculated shared image similarity and a determinedsize of the overlapping region.

The calculating of the similarity score may include calculating theshared image similarity based on a normalized cross correlation (NCC) ora phase correlation between the input fingerprint image and the matchedenrolled fingerprint image.

The calculating of the similarity score may include calculating a sharedarea ratio based on a first ratio between a size of the overlappingregion and a size of the input fingerprint image or based on a secondratio between the size of the overlapping region and a size of thematched enrolled fingerprint image, and calculating the similarity scorebased on the calculated shared area ratio and the calculated similarity.

In response to the calculated similarity meeting a similarity threshold,the shared area ratio may be the first ratio or the second ratio, inresponse to the similarity failing to meet the similarity threshold, theshared area ratio may be a predefined value, and the similarity scoremay be based on a multiplication of the shared area ratio and thecalculated similarity.

The method may further include determining whether to cease afingerprint enrollment process, including calculating an enrollmentscore of plural enrolled fingerprint images, including the one or moreenrolled fingerprint images and the stored other enrolled fingerprintimage, based on a number of the plural enrolled fingerprint images andsimilarity scores between the plural enrolled fingerprint image, anddetermining whether to cease the fingerprint enrollment process bycomparing the enrollment score to a threshold score.

The calculating of the enrollment score may include calculating theenrollment score to be at least one S_(Enroll) of:

${S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\sum\limits_{j = {i + 1}}^{m}\; s_{ij}}} + {\sum\limits_{i = 1}^{m - 2}\; {\sum\limits_{j = {i + 1}}^{m - 1}\; {\sum\limits_{k = {j + 1}}^{m}\; {s_{ij}s_{jk}}}}}}},{S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\max\limits_{i < j \leq m}s_{ij}}}}},{S_{Enroll} = {\sum\limits_{i = 1}^{m}\; \left( {1 - {\max\limits_{{1 \leq j \leq m},{j \neq i}}s_{ij}}} \right)}},{or}$${S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\sum\limits_{j = {i + 1}}^{m}\; s_{ij}}}}},$

wherein m denotes the number of the plural enrolled fingerprint images,and S_(ij) denotes a similarity score between an i-th enrolledfingerprint image and a j-th enrolled fingerprint image of the pluralenrolled fingerprint images.

The method may further include determining whether to cease afingerprint enrollment process, including updating respective degrees ofdiversity corresponding to the overlapping region of plural enrolledfingerprint images, including the one or more enrolled fingerprintimages and the stored other enrolled fingerprint image, based on thecalculated degree of diversity, calculating an effective area size ofthe plural enrolled fingerprint images based on the updated degrees ofdiversity, and determining whether to cease the fingerprint enrollmentprocess by comparing the calculated effective area size to a thresholdarea size.

A relationship between a size of the overlapping region and theeffective area size may exists such that, in response to an increase insize of overlapping region of the plural enrolled fingerprint images,the effective area size decreases, and a relationship between theupdated respective degrees of diversity and the effective area size mayexist such that, in response to an increase in the updated degrees ofdiversity, the effective area size increases.

The method may further include calculating the degree of diversity inthe fingerprint, where a result of the calculating of the degree ofdiversity may be dependent on at least one of a direction of afingerprint corresponding to the input fingerprint image, a direction, adistribution, and a magnitude of a finger pressure corresponding to theinput fingerprint image, and a humidity and/or other condition of aportion of skin corresponding to the input fingerprint image.

The matching may include obtaining at least one phase correlationbetween the input fingerprint image and the one or more enrolledfingerprint images based on a frequency-based matching method, obtainingat least one of a translation, a rotation, or a scale between the inputfingerprint image and the one or more enrolled fingerprint images basedon the obtained phase correlation, and determining whether, and whichof, any of the one or more enrolled fingerprint images match the inputfingerprint image based on at least one of the obtained translation, theobtained rotation, or the obtained scale.

The method may further include performing a recognition process tocompare the input fingerprint image or another input fingerprint imageto at least the one or more enrolled fingerprint images and selectivelyenable user access to stored information of a computing device thatperforms a fingerprint enrollment process including the selectivestoring the input fingerprint image as another enrolled fingerprintimage.

In one general aspect, a processor implemented fingerprint enrollmentmethod includes comparing an input fingerprint image of a user tomultiple enrolled fingerprint images, to recognize the input fingerprintimage as having matched correspondence to an enrolled fingerprint of theuser based on a result of the comparing, measuring respective degrees ofdiversity corresponding to one or more overlapping regions between therecognized input fingerprint image and one or matched enrolledfingerprint images of the multiple enrolled fingerprint images, anddetermining whether to enroll the input fingerprint image based on themeasured respective degrees of diversity.

In on general aspect, there is provided a non-transitorycomputer-readable storage medium storing instructions, that whenexecuted by a processor, cause the processor to perform one or more orall of the processes described herein.

In one general aspect, a fingerprint enrollment apparatus includes afingerprint sensor configured to receive an input fingerprint image of auser, and a processor configured to perform a matching of the inputfingerprint image to one or more enrolled fingerprint images, and, inresponse to the matching identifying a matched enrolled fingerprintimage, extract an overlapping region between the input fingerprint imageand the matched enrolled fingerprint image, calculate a degree ofdiversity in the fingerprint corresponding to the overlapping region,and determine whether to store the input fingerprint image as anotherenrolled fingerprint image based on the calculated degree of diversity.

The processor may be configured to calculate a similarity score betweenthe input fingerprint image and the matched enrolled fingerprint imagebased on a calculated similarity corresponding to the overlappingregion, where, a relationship between the similarity score and thedegree of diversity may exist such that, in response to an increase inthe similarity score, the degree of diversity decreases, and in responseto a decrease in the similarity score, the degree of diversityincreases.

The processor may be configured to calculate a shared image similaritybetween a first shared image portion of the input fingerprint imagecorresponding to the overlapping region and a second shared imageportion of the matched enrolled fingerprint image corresponding to theoverlapping region, and calculate the similarity score based on thecalculated shared image similarity and a determined size of theoverlapping region.

The processor may be configured to calculate a shared area ratio basedon a first ratio between a size of the overlapping region and a size ofthe input fingerprint image or based on a second ratio between the sizeof the overlapping region and a size of the matched enrolled fingerprintimage, and calculate the similarity score based on the calculated sharedarea ratio and the similarity.

The processor may be configured to calculate an enrollment score ofplural enrolled fingerprint images, including the one or more enrolledfingerprint images and the stored other enrolled fingerprint image,based on a number of the plural enrolled fingerprint images andsimilarity scores of the plural enrolled fingerprint images, anddetermine whether to cease a fingerprint enrollment process by comparingthe calculated enrollment score to a threshold score.

The processor may be configured to update respective degrees ofdiversity corresponding to the overlapping region of plural enrolledfingerprint images, including the one or more enrolled fingerprintimages and the stored other enrolled fingerprint image, based on thecalculated degree of diversity, calculate an effective area size of theplural enrolled fingerprint images based on the updated respectivedegrees of diversity, and determine whether to cease a fingerprintenrollment process by comparing the calculated effective area size to athreshold area size.

The processor may be configured to perform a recognition process tocompare the input fingerprint image or another input fingerprint imageto at least the one or more enrolled fingerprint images to selectivelyenable user access to stored information of the enrollment apparatus.

The apparatus may further include another fingerprint sensor, and theprocessor may be further configured to perform the recognition processto compare the other input fingerprint image captured by the otherfingerprint sensor to at least the one or more enrolled fingerprintimages and selectively enable user access to stored information of theenrollment apparatus based on a result of the recognition process, andthe other fingerprint sensor may have a different image capturingconfiguration than the fingerprint sensor, so as to capture a differentfingerprint image shape or size than captured by the fingerprint sensor.

In one general aspect, an enrollment apparatus includes a processorconfigured to perform a matching of an input bio-image of a user to oneor more enrolled bio-images, and, in response to the matchingidentifying a matched enrolled bio-image, extract an overlapping regionbetween the input bio-image image and the matched enrolled bio-imageimage, calculate a degree of diversity in the bio-image corresponding tothe overlapping region, and determine whether to store the inputbio-image as another enrolled bio-image image based on the calculateddegree of diversity.

The apparatus may further include a bio-sensor configured to receive theinput bio-image of the user.

The bio-sensor may be a fingerprint sensor configured to capture only aportion of a corresponding fingerprint corresponding to a sensor area ofthe fingerprint sensor, with the input bio-image being a correspondinginput fingerprint image of only the portion of the correspondingfingerprint.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 are diagrams illustrating examples of fingerprint images.

FIG. 3 is a flowchart illustrating an example of a fingerprintenrollment method.

FIG. 4 is a diagram illustrating an example of an input fingerprintimage and an enrolled fingerprint image.

FIGS. 5A and 5B are diagrams illustrating an example of imagescorresponding to an overlapping region.

FIG. 6 is a flowchart illustrating an example of a fingerprintenrollment method.

FIG. 7 is a flowchart illustrating an example of a fingerprintenrollment method.

FIG. 8 is a diagram illustrating an example of enrolled fingerprintimages.

FIG. 9 is a flowchart illustrating an example of a frequency-basedmatching method.

FIG. 10 is a flowchart illustrating an example of a fingerprintenrollment method.

FIG. 11 is a diagram illustrating an example of a fingerprint enrollmentmethod.

FIGS. 12A-12B are diagrams illustrating examples of a fingerprintenrollment apparatus.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same or like elements, features, andstructures. The drawings may not be to scale, and the relative size,proportions, and depiction of elements in the drawings may beexaggerated for clarity, illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art may be omitted forincreased clarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

The terminology used herein is for the purpose of describing particularexamples only, and is not to be used to limit the disclosure. As usedherein, the terms “a,” “an,” and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Asused herein, the terms “include,” “comprise,” and “have” specify thepresence of stated features, numbers, operations, elements, components,and/or combinations thereof, but do not preclude the presence oraddition of one or more other features, numbers, operations, elements,components, and/or combinations thereof.

Terms such as first, second, A, B, (a), (b), and the like may be usedherein to describe components. Each of these terminologies is not usedto define an essence, order or sequence of a corresponding component butused merely to distinguish the corresponding component from othercomponent(s). For example, a first component may be referred to a secondcomponent, and similarly the second component may also be referred to asthe first component.

It should be noted that if it is described in the specification that onecomponent is “connected,” “coupled,” or “joined” to another component, athird component may be “connected,” “coupled,” and “joined” between thefirst and second components, although the first component may bedirectly connected, coupled or joined to the second component. Inaddition, it should be noted that if it is described in thespecification that one component is “directly connected” or “directlyjoined” to another component, a third component may not be presenttherebetween. Likewise, expressions, for example, “between” and“immediately between” and “adjacent to” and “immediately adjacent to”may also be construed as described in the foregoing.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertainsconsistent with and after an understanding of the present disclosure.Terms, such as those defined in commonly used dictionaries, are to beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art and the present disclosure, and are notto be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

Examples to be described hereinafter may be embodied in various forms ofproducts, for example, a personal computer (PC), a laptop computer, atablet PC, a smartphone, a television (TV), a smart home appliance, anintelligent vehicle, a kiosk, and a wearable device. For example, theexamples may be applicable to user recognition used in, for example, asmartphone, a mobile device, and a smart home system. In addition, theexamples may be applicable to a payment service based on userrecognition. Further, the examples may also be applicable to a smartvehicle system that is automatically started through user recognition.Hereinafter, example embodiments are described in detail with referenceto the accompanying drawings. Like reference numerals in the drawingsdenote like elements.

FIGS. 1 and 2 are diagrams illustrating examples of fingerprint images.

Referring to FIG. 1, a fingerprint sensor senses a fingerprint 100 of auser. The fingerprint sensor senses the fingerprint 100 through asensing region. A size of the sensing region of the fingerprint sensormay be smaller than a size of the fingerprint 100. For example, thesensing region of the fingerprint sensor may be a rectangular shapesmaller than the size of the fingerprint 100. In such an example, thefingerprint sensor may sense a portion of the fingerprint 100 throughthe sensing region.

The fingerprint sensor may generate an image by capturing a sensedfingerprint, and the captured image may herein be referred to as aninput fingerprint image, for example. In response to the size of thesensing region of the fingerprint sensor being less than the size of thefingerprint 100, the fingerprint image generated by the fingerprintsensor may correspond to a partial image including a portion of thefingerprint 100.

The fingerprint image may be used to enroll or recognize the fingerprint100. For example, the fingerprint image may be enrolled in an enrollmentprocess. The enrolled fingerprint image may be stored in a storageprovided in advance. In response to the size of the sensing region ofthe fingerprint sensor being less than the size of the fingerprint 100,a plurality of fingerprint images corresponding to a plurality ofpartial images of the fingerprint 100 of the user may be enrolled. Forexample, as illustrated in FIG. 1, a plurality of fingerprint images,for example, fingerprint images 110 through 170, may be enrolled. Eachof the fingerprint images 110 through 170 may cover a portion of thefingerprint 100, and the fingerprint images 110 through 170 incombination may cover an entirety of the fingerprint 100. Thefingerprint images 110 through 170 may overlap one another. Hereinafter,a fingerprint image already enrolled is referred to as an enrolledfingerprint image.

In addition, an input fingerprint image may be recognized in arecognition process. For example, the input fingerprint image may becompared to an enrolled fingerprint image in the recognition process. Aresult of an authenticating or identifying of a user may be obtainedbased on whether the input fingerprint image corresponds to the enrolledfingerprint image, a corresponding computing apparatus may enable a userto access additional functions of the computing apparatus, such as toaccess stored information or implement other functions of the computingapparatus. In response to the size of the sensing region of thefingerprint sensor being less than the size of the fingerprint 100, forexample, or the sensing region being less than the size of thefingerprint 100 for other selective reasons, the input fingerprint imagemay correspond to a partial image of the fingerprint 100 of the user.

Although the sensing region of the fingerprint sensor is illustrated ashaving a rectangular shape in FIG. 1, a size and a shape of the sensingregion of the fingerprint sensor may vary. For example, as illustratedin FIG. 2, the sensing region of the fingerprint sensor may have acircular shape. In addition, depending on the configuration of thefingerprint sensor, different sizes and shapes may be sensed by a samefingerprint sensor. In an example of FIG. 2, where all of the sensingregions have circular shapes, in the enrollment process, a plurality offingerprint images, for example, fingerprint images 210 through 295,corresponding to a single fingerprint 200 may be enrolled. In addition,during the subsequent recognition process, a fingerprint imagecorresponding to a portion of the fingerprint 200 may be compared to theenrolled fingerprint images 210 through 295.

In addition, depending on embodiment, the fingerprint sensor used in theenrollment process may differ from the fingerprint sensor used in therecognition process. For example, the fingerprint sensor having therectangular-shaped sensing region as illustrated in FIG. 1 may be usedin the enrollment process, and the fingerprint sensor having thecircular-shaped sensing region as illustrated in FIG. 2 may be used inthe recognition process.

FIG. 3 is a flowchart illustrating an example of a fingerprintenrollment method.

Referring to FIG. 3, in operation 310, a fingerprint enrollmentapparatus receives an input fingerprint image to be used for fingerprintenrollment. The fingerprint enrollment apparatus refers to an apparatusused to enroll or recognize a fingerprint of a user. The fingerprintenrollment apparatus may include a fingerprint sensor and a processor,where, for example, the processor may be controlled to implement one ormore or all fingerprint enrollment processes and methods describedherein through execution of a software module, or such enrollmentprocesses may be implemented through computing hardware that isconfigured to implement such fingerprint enrollment processes throughhardware alone without such executable software. The fingerprintenrollment apparatus may receive the input fingerprint image from thefingerprint sensor, and the input fingerprint image may be a fingerprintimage captured by the fingerprint sensor that may indicate only aportion of a fingerprint of a user, for example. As described withreference to FIGS. 1 and 2, depending on embodiment, a size of a sensingregion of the fingerprint sensor may be smaller than a size of thefingerprint of the user, and thus a size of the fingerprint image may besuch so as to illustrate less than the full fingerprint.

In operation 320, the fingerprint enrollment apparatus matches the inputfingerprint image to an enrolled fingerprint image. The matching of theinput fingerprint image to the enrolled fingerprint image refers to anoperation of searching, based on the input fingerprint image, for aportion of any of the enrolled fingerprint images shared with the inputfingerprint image, and may further include, scaling up or down,rotating, and/or translating of the input fingerprint image relative tothe enrolled fingerprint images to overlap the shared portion. Anexample of a method of the matching will be described with reference toFIG. 9. The fingerprint enrollment apparatus may obtain a plurality ofenrolled fingerprint images from a database provided in advance. Thedatabase may be embodied as a memory included in the fingerprintenrollment apparatus, or an external device, for example, a serverconnectable to the fingerprint enrollment apparatus through a wire or anetwork, or wirelessly through a user interface of the fingerprintenrollment apparatus. In addition, in an example, the enrollment processmay further include an initial operation where a first receivedfingerprint image is stored as an enrolled fingerprint image.

Accordingly, with such circumstance, it is found that there is a desirefor technology for enrolling and verifying a fingerprint using a sensorconfigured to sense a portion of a fingerprint. However, due to currenttechnological approaches, current enrollment and/or recognitionprocesses are inefficient and slow. Accordingly, one or more embodimentsdescribed herein may improve on such technological failures and improvefingerprint enrollment and recognition efficiency and/or speed.

Referring to FIG. 4, the fingerprint enrollment apparatus matches aninput fingerprint image 410 to an enrolled fingerprint image 420. Thefingerprint enrollment apparatus extracts a shared portion between theinput fingerprint image 410 and the enrolled fingerprint image 420 fromthe input fingerprint image 410 and the enrolled fingerprint image 420,and scales up or down, rotates, and/or translates the input fingerprintimage 410 to allow extracted respective shared portions to overlap eachother. In this example, the enrolled fingerprint image 420 may be one ofa plurality of enrolled fingerprint images stored in a database, and thefingerprint enrollment apparatus may perform the matching operation ofthe input fingerprint image 410 to each of the enrolled fingerprintimages.

Referring back to FIG. 3, in operation 330 and based on results of thematching operation, the fingerprint enrollment apparatus extracts anoverlapping region between the matched input fingerprint image and theenrolled fingerprint image, i.e., between the input fingerprint imageand the resultant matched enrolled fingerprint image identified in thematching operation. The overlapping region, for example, an overlappingregion 430 as illustrated in FIG. 4, refers to a spatial area with asize corresponding to the shared portion between the input fingerprintimage 410 and the enrolled fingerprint image 420. The overlapping region430 may be distinguished between a partial image of the inputfingerprint image 410 and a partial image of the enrolled fingerprintimage 420, such that the overlapping region 430 can be considered fromthe perspective of the partial image of the input fingerprint image 410and from the perspective of the partial image of the enrolledfingerprint image 420.

Thus, referring back to FIG. 3, in operation 340, the fingerprintenrollment apparatus measures a degree of diversity in the fingerprintof the user corresponding to the overlapping region 430. The degree ofdiversity in the fingerprint corresponding to the overlapping region 430may be a measure indicating how diverse images correspond to theoverlapping region. Accordingly, an image corresponding to theoverlapping region 430 from the perspective of the matched inputfingerprint image 410, i.e., in the matched fingerprint image 410, isreferred to as a first shared image, and an image corresponding to theoverlapping region 430 from the perspective of the enrolled fingerprintimage 420, i.e., in the enrolled fingerprint image 420, is referred toas a second shared image. The fingerprint enrollment apparatus measuresthe degree of diversity corresponding to the overlapping region 430based on the first shared image and the second shared image. In responseto a determination that there is great difference between informationincluded in the first shared image and information included in thesecond shared image, the degree of diversity in the fingerprint of theuser corresponding to the overlapping region 430 may be determined to begreat.

Referring to FIG. 5A, an image 510 and an image 520 are imagescorresponding to an overlapping region in an input fingerprint image andin an enrolled fingerprint image. For example, the image 510 maycorrespond to the first shared image corresponding to the overlappingregion 430 in the matched input fingerprint image 410 of FIG. 4, and theimage 520 may correspond to the second shared image corresponding to theoverlapping region 430 in the enrolled fingerprint image 420 of FIG. 4.Although the image 510 and the image 520 correspond to the overlappingregion 430 and represent a same fingerprint, a fingerprint distributionof a portion 530 of the image 510 may be concentrated in one directioncompared to a portion 540 of the image 520, such as due to thedistribution of pressure by a finger becoming different when inputtingthe fingerprint. The fingerprint enrollment apparatus measures a degreeof diversity in a fingerprint of a user corresponding to the overlappingregion 430 based on information included in the portion 530 of the image510 and information included in the portion 540 of the image 520.

Referring to FIG. 5B, an image 550 and an image 560 are also respectivefirst and second shared images corresponding to an overlapping region430 in an input fingerprint image and in an enrolled fingerprint image.Although the image 550 and the image 560 correspond to the overlappingregion 430 illustrated in FIG. 4 and represent a same fingerprint, adistance between ridges of the fingerprint may be different in responseto the magnitude of pressure of the finger becoming different wheninputting the fingerprint, as observed through a comparison between aportion 570 of the image 550 and a portion 580 of the image 560. In sucha case, the image 550 and the image 560 may be determined to representdifferent textures. The fingerprint enrollment apparatus measures adegree of diversity in a fingerprint of a user corresponding to theoverlapping region 430 based on information included in the portion 570of the image 550 and a portion 580 of the image 560.

As illustrated in FIGS. 5A and 5B, fingerprint images representing asame fingerprint may include different portions having differentdiversity conditions dependent on the situations and conditions when thefingerprint was input, for example, at least one of a direction of thefingerprint, a direction and a magnitude of a pressure of a finger, ahumidity and/or other conditions of a corresponding portion of skin. Inaddition, a fingerprint image may include a portion partially degradeddue to various reasons. For example, the fingerprint image may bedeformed by pressure generated by the pressing of the finger on thesensor.

When an input fingerprint image is generated, pressures applied to eachportion of a sensing region of a fingerprint sensor may vary. Thus, atleast a portion of the input fingerprint image may be deformed. Inaddition, an enrolled fingerprint image may be degraded due to variousreasons.

Although the first shared image, for example, the image 510 or the image550, and the second shared image, for example, the image 520 or theimage 560, include different portions corresponding to a samefingerprint, the fingerprint enrollment apparatus may employ a method ofincreasing a degree of diversity in a fingerprint of a usercorresponding to the overlapping region 430 without excluding the inputfingerprint image from a target to be enrolled in a fingerprintenrollment process, and thus increase a speed of the fingerprintenrollment process. Rather, previously such an input fingerprint image410 corresponding to either or both of image 510 and 550 may have beenexcluded from enrollment. As noted, the degree of diversity may beaffected by a situation and condition of a time at which the fingerprintis input.

Referring back to FIG. 4, based on a similarity corresponding to theoverlapping region 430, the fingerprint enrollment apparatus calculatesa similarity score between the matched input fingerprint image 410 andthe enrolled fingerprint image 420. The fingerprint enrollment apparatusmeasures a degree of diversity in a fingerprint of a user based on thecalculated similarity score. For example, a greater similarity score mayrepresent a lower degree of diversity and a lower similarity score mayrepresent a greater degree of diversity, or said another way, inresponse to an increase in the similarity score, the degree of diversitymay decrease, and in response to a decrease in the similarity score, thedegree of diversity may increase. Based on such a relationship betweenthe similarity score and the degree of diversity, the fingerprintenrollment apparatus may measure the degree of diversity in thefingerprint of the user.

The fingerprint enrollment apparatus calculates a similarity between thefirst shared image and the second shared image. For example, thefingerprint enrollment apparatus may calculate the similarity betweenthe first shared image and the second shared image based on a normalizedcross correlation (NCC) or a phase correlation between the matched inputfingerprint image 410 and the enrolled fingerprint image 420. Thefingerprint enrollment apparatus may calculate an image brightnessvalue-based NCC using the below Equation 1, for example.

$\begin{matrix}{{{ncc}\left( {I_{1},I_{2}} \right)} = \frac{\Sigma_{{({i,j})} \in W}\mspace{14mu} {{I_{1}\left( {i,j} \right)}.{I_{2}\left( {{x + i},{y + j}} \right)}}}{\sqrt[2]{\Sigma_{{({i,j})} \in W}\mspace{14mu} {{I_{1}^{2}\left( {i,j} \right)}.\Sigma_{{({i,j})} \in W}}\mspace{14mu} {I_{2}^{2}\left( {{x + i},{y + j}} \right)}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1, ncc(I₁, I₂) denotes an NCC between an image I₁ and animage I₂, and W denotes an overlapping region between the image I₁ andthe image I₂. A value of ncc(I₁, I₂) is closer to 1 when the image I₁and the image I₂ are more similar in the overlapping region W.

In Equation 1, image I₁ may correspond to the matched input fingerprintimage 410, and the image I₂ may correspond to the enrolled fingerprintimage 420, with i and j denoting an x-axis coordinate and a y-axiscoordinate, respectively, of a pixel in the overlapping region W. Thevariables x and y denote translation information in an x-axis direction(Tx) and translation information in a y-axis direction (Ty),respectively. I₁(i, j) denotes a pixel value in (i, j) coordinates ofthe image I₁, and I₂(x+i, y+j) denotes a pixel value in (x+i, y+j)coordinates. The NCC between the image I₁ and the image I₂ indicates acorrelation corresponding to the overlapping region between the matchedinput fingerprint image 410 and the enrolled fingerprint image 420.Thus, the fingerprint enrollment apparatus may employ, as the similaritybetween the first shared image and the second shared image, the NCCbetween the image I₁ and the image I₂ that is calculated using Equation1.

The fingerprint enrollment apparatus may calculate a similarity scorebetween the matched fingerprint image 410 and the enrolled fingerprintimage 420 based on the calculated similarity and a size of theoverlapping region 430. In an example, the fingerprint enrollmentapparatus calculates a shared area ratio based on a ratio between thesize of the overlapping region 430 and a size of the matched inputfingerprint image 410 (hereinafter referred to as a first ratio), or aratio between the size of the overlapping region 430 and a size of theenrolled fingerprint image 420 (hereinafter referred to as a secondratio). In an example, the size of the input fingerprint image 410 andthe size of the enrolled fingerprint image 420 may be defined by a sizeof a sensing region of a fingerprint sensor, and thus may be equal toeach other. In such a case, the first ratio and the second ratio may beequal to each other.

In another example, the fingerprint enrollment apparatus calculates theshared area ratio based on a ratio between the size of the overlappingregion 430 and a sum of the size of the matched input fingerprint image410 and the size of the enrolled fingerprint image 420. In a case thatthe size of the matched input fingerprint image 410 and the size of theenrolled fingerprint image 420 differ from each other, using the sum ofthe size of the matched input fingerprint image 410 and the size of theenrolled fingerprint image 420 may be effective.

Accordingly, the fingerprint enrollment apparatus may calculate thesimilarity score based on the shared area ratio and the similarity. Forexample, in response to the calculated similarity meeting a thresholdsimilarity, e.g., being greater than or equal to the thresholdsimilarity, the fingerprint enrollment apparatus measures the sharedarea ratio based on the first ratio and the second ratio. Conversely, inresponse to the calculated similarity not meeting the thresholdsimilarity, e.g., being less than the threshold similarity, thefingerprint enrollment apparatus sets the shared area ratio to be apredefined value. In response to the calculated similarity between thefirst shared image and the second shared image being excessively small,for example, the calculated similarity failing to meet the thresholdsimilarity, the first shared image and the second shared image may beexcluded from the measuring of the degree of diversity in thefingerprint of the user. The fingerprint enrollment apparatus maycalculate the shared area ratio using the below Equation 2, for example.

$\begin{matrix}{{r_{overlap}\left( {I_{1},I_{2}} \right)} = \left\{ {\frac{{size}\mspace{14mu} {of}\mspace{14mu} {overlapping}\mspace{14mu} {area}}{\begin{matrix}{{size}\mspace{14mu} {of}\mspace{14mu} {input}\mspace{14mu} {fingerprint}\mspace{14mu} {image}\mspace{14mu} {or}} \\{{size}\mspace{14mu} {of}\mspace{14mu} {enrolled}\mspace{14mu} {fingerprint}\mspace{14mu} {image}} \\{{{constant}\mspace{14mu} \left( {{ex}.\mspace{14mu} 0} \right)},{otherwise}}\end{matrix}},{{{if}\mspace{14mu} {{ncc}\left( {I_{1},I_{2}} \right)}} > {ncc}_{thres}}} \right.} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In Equation 2, r_(overlap)(I₁, I₂) denotes a shared area ratio betweenan image I₁ and an image I₂, and the image I₁ may be the matched inputfingerprint image 410 and the image I₂ may be the enrolled fingerprintimage 420. As noted above, ncc(I₁, I₂) denotes a similarity between theimage I₁ and the image I₂, and, thus, ncc_(thres) denotes a thresholdsimilarity. For example, if a size of an input fingerprint image or asize of an enrolled fingerprint image is 10 and a size of an overlappingregion is 7, a value of r_(overlap)(I₁, I₂) may be 0.7.

The fingerprint enrollment apparatus may calculate a similarity scorebased on a multiplication of the shared area ratio and the similarity.The fingerprint enrollment apparatus may calculate the similarity scoreusing the below Equation 3, for example.

s ₁₂ =s _(overlap)(I ₁ ,I ₂)=ncc(I ₁ ,I ₂)×r _(overlap)(I ₁ ,I₂)  Equation 3:

In Equation 3, s₁₂ and s_(overlap)(I₁, I₂) denote a similarity scorebetween an image I₁ and an image I₂, and the image I₁ may be the matchedinput fingerprint image 410 and the image I₂ may be the enrolledfingerprint image 420. The ncc(I₁, I₂) denotes the similarity betweenthe image I₁ and the image I₂, and the r_(overlap)(I₁, I₂) denotes ashared area ratio between the image I₁ and the image I₂.

The fingerprint enrollment apparatus calculates the similarity scorebetween the matched input fingerprint image 410 and the enrolledfingerprint image 420, and measures the degree of diversity in thefingerprint of the user corresponding to the overlapping region 430using the calculated similarity score. Although the calculating of thesimilarity score is described above, examples of a method of measuring adegree of diversity in a fingerprint of a user is not limited thereto.Depending on embodiment, various applied methods may be applied todefine a degree of diversity in information corresponding to theoverlapping region 430.

Referring back to FIG. 3, in operation 350, the fingerprint enrollmentapparatus determines whether to complete a fingerprint enrollmentprocess for the example fingerprint image 410 based on the degree ofdiversity. The fingerprint enrollment process refers to a process ofenrolling fingerprints, of which one or more or all may beneeded/required to be matched to identify or verify a fingerprint inputin a recognition process. When information quantities of enrolledfingerprint images and related values satisfy a preset standard, thefingerprint enrollment process may be completed. For example, when anumber of enrolled fingerprint images representing a same fingerprint ofa user exceeds a predefined number, or a size of area region covered bythe enrolled fingerprint images exceeds a predefined size, thefingerprint enrollment process may be completed and thus cease. In anexample, upon cessation, the fingerprint enrollment apparatus mayindicate to the user through a user interface that the fingerprintenrollment for the current fingerprint is complete, and may proceed toenrollment of a next fingerprint or cease all fingerprint enrollments.

The fingerprint enrollment apparatus may determine whether to completeor cease the fingerprint enrollment process based on the degrees ofdiversity corresponding to overlapping regions among enrolledfingerprint images identified in the matching process, for example.Thus, with the successful enrollment of fingerprint image 410 discussedabove with operations of FIG. 3, enrolled fingerprint images may includethe input fingerprint image 410 and the enrolled fingerprint image 420,for example, and the fingerprint enrollment apparatus may measurerespective degrees of diversity of the enrolled fingerprint images. Forexample, the fingerprint enrollment apparatus may calculate anenrollment score based on the degrees of diversity, and determinewhether to now complete or cease the fingerprint enrollment process,after the addition of the input fingerprint image to the stored enrolledfingerprint images, using the calculated enrollment score. An example ofusing an enrollment score calculated based on similarity scores isdescribed with reference to FIG. 6.

FIG. 6 is a flowchart illustrating an example of a fingerprintenrollment method.

Referring to FIG. 6, in operation 610, the fingerprint enrollmentapparatus calculates an enrollment score based on a number of enrolledfingerprint images and similarity scores between the enrolledfingerprint images. The enrolled fingerprint images may include, forexample, the matched input fingerprint image 410 and the enrolledfingerprint image 420 of FIG. 4, and the similarity scores may becalculated through matching of image portions between the enrolledfingerprint images by applying the methods described above with respectto FIGS. 3-5. For example, when the number of the enrolled fingerprintimages is m, the number of similarity scores may be _(m)C₂.

In operation 620, the fingerprint enrollment apparatus determineswhether to complete or cease the fingerprint enrollment process bycomparing the enrollment score, for example, S_(Enroll), to a thresholdscore, for example, Th_(Enroll). In operation 630, in response to theenrollment score meeting, e.g., exceeding, the threshold score, thefingerprint enrollment apparatus completes the fingerprint enrollmentprocess. In response to the enrollment score failing to meet, e.g., notexceeding, the threshold score, the fingerprint enrollment apparatusreceives, e.g., requests for and receives, a new input fingerprint imageto be used for fingerprint enrollment, and measures a degree ofdiversity of the new input fingerprint with respect to each matchingenrolled fingerprint image.

As described above, to determine whether a sufficient region of thewhole fingerprint has been input, e.g., by the repetitively receivingand enrollment considering of input fingerprints of the enrolled user, ashared region between enrolled images may be calculated, and a scorecorresponding to diversity may be applied. Various methods may be usedto calculate a total enrolled size of an input fingerprint using aninput size of a partial fingerprint. For example, when a fingerprint ofa user is input m number of times, the enrollment apparatus maycalculate the enrollment score based on at least one of the belowEquations 4 through 7, as only examples. Here, an independent input mayincrease by 1, and a sharing degree with already input fingerprintimages may be excluded.

$\begin{matrix}{S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\sum\limits_{j = {i + 1}}^{m}\; s_{ij}}} + {\sum\limits_{i = 1}^{m - 2}\; {\sum\limits_{j = {i + 1}}^{m - 1}\; {\sum\limits_{k = {j + 1}}^{m}\; {s_{ij}s_{jk}}}}}}} & {{Equation}\mspace{14mu} 4} \\{\mspace{76mu} {S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\max\limits_{i < j \leq m}s_{ij}}}}}} & {{Equation}\mspace{14mu} 5} \\{\mspace{76mu} {S_{Enroll} = {\sum\limits_{i = 1}^{m}\; \left( {1 - {\max\limits_{{1 \leq j \leq m},{j \neq i}}s_{ij}}} \right)}}} & {{Equation}\mspace{14mu} 6} \\{\mspace{76mu} {S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\sum\limits_{j = {i + 1}}^{m}\; s_{ij}}}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

In the equations above, m denotes a number of enrolled fingerprintimages, and S_(ij) denotes a similarity score between an i-th enrolledfingerprint image and a j-th enrolled fingerprint image.

FIG. 7 is a flowchart illustrating an example of a fingerprintenrollment method.

Referring to FIG. 7, in operation 710, the fingerprint enrollmentapparatus calculates an effective area size of the enrolled fingerprintimages. The effective area size may be defined as an area size obtainedby applying the respective degrees of diversity of the enrolledfingerprint images to a total area size of the enrolled fingerprintimages. The total area size of the enrolled fingerprint images refers toan area size covered by the enrolled fingerprint images, and may becalculated by subtracting sizes of regions added by overlapping, forexample, overlapping regions, from a total sum of area sizes of theenrolled fingerprint images by the number of times of the overlapping.

FIG. 8 illustrates a plurality of enrolled fingerprint images 810 thatoverlaps one another and a plurality of enrolled fingerprint images 820that overlaps one another. Although a number of the enrolled fingerprintimages 810 is equal to a number of the enrolled fingerprint images 820,the enrolled fingerprint images 820 are distributed more densely thanthe enrolled fingerprint images 810, and thus a total area size of theenrolled fingerprint images 810 is greater than a total area size of theenrolled fingerprint images 820. However, in response to respectivedegrees of diversity of the enrolled fingerprint images 820 beinggreater than respective degrees of diversity of the enrolled fingerprintimages 810, an effective area size of the enrolled fingerprint images820 may be greater than an effective area size of the enrolledfingerprint images 810.

In an example, the fingerprint enrollment apparatus may extractoverlapping regions of the current enrolled fingerprint images, e.g.,now including the matched input fingerprint image, and update therespective degrees of diversity corresponding to the extractedoverlapping regions. For example, the fingerprint enrollment apparatusmay have extracted an overlapping region between a newly matched inputfingerprint image, i.e., an input fingerprint image just stored asanother of the enrolled fingerprint images, and each of the thenexisting enrolled fingerprint images excluding the input fingerprintimage, and calculate the respective degrees of diversity correspondingto the extracted overlapping regions. The fingerprint enrollmentapparatus may update the respective degrees of diversity correspondingto the overlapping regions of all current enrolled fingerprint imagesincluding the matched input fingerprint image by applying the calculateddegrees of diversity to prestored degrees of diversity. The degrees ofdiversity may be calculated based on the similarity scores describedabove, but is not limited thereto.

The fingerprint enrollment apparatus may calculate an effective areasize of the current enrolled fingerprint images based on the updateddegrees of diversity. For example, the fingerprint enrollment apparatusmay calculate the effective area size by defining respective weightsbased on the degrees of diversity and applying the respective weights tosizes of the overlapping regions when calculating a total are size ofthe current enrolled fingerprint images. Alternatively, the fingerprintenrollment apparatus may calculate the effective area size by defining acorrection value based on the degrees of diversity, and applying thecorrection value to the total area size of the current enrolledfingerprint images, for example, by adding or multiplying the correctionvalue. Alternatively, the fingerprint enrollment apparatus may calculatethe effective area size by applying the method of calculating theenrollment score described with reference to FIG. 6, but examples arenot limited thereto. In response to an increase in the sizes of theoverlapping regions of the current enrolled fingerprint images, theeffective area size may decrease. In response to an increase in thedegrees of diversity, the effective area size may increase.

Referring back to FIG. 8, comparing between the fingerprint enrollmentapparatus measuring respective degrees of diversity corresponding tooverlapping regions of the enrolled fingerprint images 810 andrespective degrees of diversity corresponding to overlapping regions ofthe enrolled fingerprint images 820, and calculating an effective areasize of the enrolled fingerprint images 810 and an effective area sizeof the enrolled fingerprint images 820, although the enrolledfingerprint images 820 are more densely distributed than the enrolledfingerprint images 810, the calculated effective area size of theenrolled fingerprint image 820 may be to be greater than the calculatedeffective area size of the enrolled fingerprint images 810. For example,images corresponding to the overlapping regions of the enrolledfingerprint images 820 may include more images that are deformed asillustrated in FIGS. 5A and 5B, resulting in the effective area size ofthe enrolled fingerprint images 820 being calculated to have a greatervalue than the effective area size of the enrolled fingerprint images810.

Referring back to FIG. 7, in operation 720, the fingerprint enrollmentapparatus determines whether to complete or cease the fingerprintenrollment process by comparing the effective area size, for example,S_(Area), of the enrolled fingerprint images to a threshold area size,for example, Th_(Area). In operation 730, in response to the effectivearea size meeting, e.g., exceeding, the threshold area size, thefingerprint enrollment apparatus completes or ceases the fingerprintenrollment process. Conversely, in response to the effective area sizefailing to meet, e.g., not exceeding, the threshold area size, thefingerprint enrollment apparatus may request another input fingerprintand receive a corresponding new input fingerprint image to be used forfingerprint enrollment and measures a degree of diversity. Here, forexample, the fingerprint enrollment apparatus may include a display anda graphical user interface, or other signaling mechanism, to indicate tothe user whether the enrollment process is complete or whether tocontinue to input fingerprint images.

FIG. 9 is a flowchart illustrating an example of a frequency-basedmatching method.

The fingerprint enrollment apparatus may employ a frequency-basedmatching method to match a fingerprint image to another fingerprintimage. For example, as illustrated in FIG. 4, using the frequency-basedmatching method, the fingerprint enrollment apparatus may perform amatching between the input fingerprint image 410 and each of the storedenrolled fingerprint images corresponding to the fingerprint, resultingin the identification of at least the enrolled fingerprint image 420 asmatch. Here, it is noted that the matching may also result in multiplematches of the stored enrolled fingerprint images. Hereinafter, aprocess of matching an input fingerprint image to an enrolledfingerprint image will be described.

Referring to FIG. 9, in operations 911 and 912, the fingerprintenrollment apparatus respectively applies a fast Fourier transform (FFT)to an input fingerprint image and an enrolled fingerprint image, andconverts time-domain information of the input fingerprint image and theenrolled fingerprint image to frequency-domain information. An imageobtained through the FFT and the converting is simply referred to as anFFT image. The frequency-domain information may be based on anorthogonal coordinate system representing information using (x, y)coordinates.

In operations 921 and 922, the fingerprint enrollment apparatusrespectively applies a log-polar transform (LPT) to the FFT image of theinput fingerprint image and the FFT image of the enrolled fingerprintimage, and coverts the coordinate system of the frequency-domaininformation of each of the FFT images to a polar coordinate system. Animage obtained through the LPT and the converting is simply referred toas an LPT image.

For example, the LPT may be performed on a magnitude of each pixel inthe FFT image obtained by the FFT. The polar coordinate system mayrepresent information using a radius, an angle, or a combinationthereof.

In operations 931 and 932, the fingerprint enrollment apparatusrespectively applies an FFT to each of the LPT image of the inputfingerprint image and the LPT image of the enrolled fingerprint image.In operation 940, the fingerprint enrollment apparatus performs a phasecorrelation between the images to which the FFT is applied, andgenerates rotation information θ between the input fingerprint image andthe enrolled fingerprint image. As a result of performing the phasecorrelation, a peak is detected, and a location of the detected peak mayindicate the rotation information θ between the input fingerprint imageand the enrolled fingerprint image.

In another example, the location of the detected peak may indicate scaleinformation between the input fingerprint image and the enrolledfingerprint image. For example, one axis of the LPT image may correspondto an angle, and another axis of the LPT image may correspond to aradius. In such an example, the location of the peak detected throughthe phase correlation may be represented by coordinates of the axiscorresponding to the angle and coordinates of the axis corresponding tothe radius. The coordinates of the axis corresponding to the angle mayindicate the rotation information θ, and the coordinates of the axiscorresponding to the radius may indicate the scale information.

In general, there may be no practical change in a scale of a fingerprintimage, and thus a radius may be fixed to a preset value, for example, 1.In such a case, the location of the peak detected through the phasecorrelation may be represented by the coordinates of the axiscorresponding to the angle. The coordinates of the axis corresponding tothe angle may indicate the rotation information θ.

In operation 950, the fingerprint enrollment apparatus rotates the inputfingerprint image based on the rotation information θ. In operation 960,the fingerprint enrollment apparatus applies an FFT to the rotated inputfingerprint image. In operation 970, the fingerprint enrollmentapparatus performs a phase correlation between the FFT image of therotated input fingerprint image and the FFT image of the enrolledfingerprint image, and generates translation information (Tx, Ty)between the rotated input fingerprint image and the enrolled fingerprintimage. A location of a peak detected as a result of performing the phasecorrelation may indicate the translation information (Tx, Ty) betweenthe rotated input fingerprint image and the enrolled fingerprint image.In operation 980, the fingerprint enrollment apparatus translates therotated input fingerprint image based on the translation information(Tx, Ty). The fingerprint enrollment apparatus may then calculatesimilarity scores between enrolled fingerprint images or degrees ofdiversity based on a result of the matching.

FIG. 10 is a flowchart illustrating an example of a fingerprintenrollment method.

Referring to FIG. 10, in operation 1010, the fingerprint enrollmentapparatus, for example, a fingerprint enrollment apparatus 1100 to bedescribed with reference to FIG. 11, receives an input fingerprint imageto be used for fingerprint enrollment or recognition, separate examplesof which are discussed below. To provide fingerprint-basedauthentication, the fingerprint enrollment apparatus may store, in adatabase, enrolled fingerprint images representing a same fingerprint ofa user. As described above, when a quantity of information associatedwith the fingerprint of the user is sufficient to identify thefingerprint, the fingerprint enrollment apparatus may complete or ceasethe fingerprint enrollment process. The fingerprint recognition processmay be performed, for example, when the fingerprint enrollment processis completed or ceased and all enrolled fingerprint images have beenstored in the database. In an example, an input fingerprint image thatis obtained while performing such user authentication may also beenrolled. For example, existing enrolled fingerprint images may beupdated, as discussed above, also during a user authentication processwhere the user is requested to input a fingerprint image forauthentication, even when enrollment has previously been completed orceased.

Referring to FIG. 11, the fingerprint enrollment apparatus 1100 includesa fingerprint sensor 1110. The fingerprint enrollment apparatus 1100 maycontrol or implement operations to be performed for fingerprintenrollment and/or recognition, and thus may also be referred to as afingerprint recognition apparatus. Additionally, embodiments may beimplemented with separate fingerprint enrollment apparatus(es) andseparate fingerprint recognition apparatus(es).

As illustrated in FIG. 11, in the example fingerprint enrollment processthe fingerprint enrollment apparatus 1100 receives an input fingerprintimage 1115 through the fingerprint sensor 1110, and a database 1120stores a plurality of enrolled fingerprint images, for example, enrolledfingerprint images 1121 through 1123. The fingerprint enrollmentapparatus 1100 selectively updates the enrolled fingerprint images 1121through 1123 by enrolling the input fingerprint image 1115 based ondetermined degrees of diversity between the input fingerprint image 1115and the enrolled fingerprint images 1121 through 1123. In such a case,one of the enrolled fingerprint images 1121 through 1123 stored in thedatabase 1120 may be replaced with the input fingerprint image 1115, orthe input fingerprint image 1115 may be added to the enrolledfingerprint images 1121 through 1123. Thus, the fingerprint enrollmentapparatus 1100 updates the existing enrolled fingerprint images 1121through 1123 to add or replace one of the existing enrolled fingerprintimages with the input fingerprint image 1115, and updates an enrollmentscore or an effective area size of the updated enrolled fingerprintimages. Here, depending on the enrollment score and/or effective areasize, the fingerprint enrollment process may complete or cease.

The fingerprint enrollment apparatus 1100 may then perform thefingerprint recognition process and recognize the input fingerprintimage 1115 or another input fingerprint image as corresponding to anenrolled fingerprint of the user by comparing the input fingerprintimage 1115 or the other fingerprint image to the updated enrolledfingerprint images. The fingerprint enrollment apparatus 1100 mayperform a matching operation, as discussed above, with respect to theinput fingerprint image 1115 or the other fingerprint image and theupdated enrolled fingerprint images to compare the input fingerprintimage 1115 or the other fingerprint image to the updated enrolledfingerprint images. The fingerprint enrollment apparatus 1100 mayrecognize, or not recognize, the fingerprint of the user based on aresult of the matching. Alternatively, as the fingerprint enrollmentprocess may have been completed or ceased upon addition of the inputfingerprint image 1115 to the enrolled fingerprint images, therecognition process could be configured to automatically recognize theuser upon completion of the fingerprint enrollment process. In addition,as noted above, during the fingerprint recognition process, diversitybetween the example other input fingerprint image and the existingenrolled fingerprint images may further be calculated, and the otherinput fingerprint image selectively added to the collection of enrolledfingerprint images or made to replace an existing enrolled fingerprintimage.

Returning to the example fingerprint enrollment process with respect toFIGS. 10 and 11, the example input fingerprint image 1115 is receivedoperation 1010, and in operation 1020, the fingerprint enrollmentapparatus 1100 may perform the matching process to recognize the inputfingerprint image 1115 as corresponding to an enrolled fingerprint ofthe user by comparing the input fingerprint image 1115 to the enrolledfingerprint images 1121 through 1123. The fingerprint enrollmentapparatus 1100 performs the matching operation, as discussed above, withrespect to the input fingerprint image 1115 to the enrolled fingerprintimages 1121 through 1123 to compare the input fingerprint image 1115 tothe enrolled fingerprint images 1121 through 1123. The fingerprintenrollment apparatus 1100 may recognize, or not recognize, thefingerprint of the user based on a result of the matching.

In operation 1030, when the input fingerprint image 115 has been matchedwith at least one of the enrolled fingerprint images 1121 through 1123,the fingerprint enrollment apparatus 1100 measures degrees of diversitycorresponding to overlapping regions between the recognized inputfingerprint image 1115 and the corresponding matched enrolledfingerprint images 1121 through 1123. Here, any of the above describedmethods of measuring the degrees of diversity may be implemented.

In operation 1040, the fingerprint enrollment apparatus 1100 determineswhether to enroll the input fingerprint image 1115 based on the measureddegrees of diversity. In an example, the fingerprint enrollmentapparatus 1100 may compare the existing enrollment score or existingeffective area size of the current enrolled fingerprint images 1121through 1123 to an enrollment score or an effective area size of theupdated enrolled fingerprint images including the input fingerprintimage 1115. In response to determined increase in the enrollment scoreor the effective area size, over the current enrollment score oreffective area size, meeting or exceeding a threshold value, thefingerprint enrollment apparatus 1100 may enroll the input fingerprintimage 1115 and update the enrolled fingerprint images 1121 through 1123.Alternatively, the fingerprint enrollment apparatus 1100 may replace,with the input fingerprint image 1115, one of the enrolled fingerprintimages 1121 through 1123, for example, an enrolled fingerprint imagethat has a determined smallest value of a contribution to the enrollmentscore or the effective area size. The fingerprint enrollment apparatus1100 enrolls the input fingerprint image 1115 to update the enrolledfingerprint images 1121 through 1123.

FIGS. 12A and 12B are diagrams illustrating examples of a fingerprintenrollment apparatus.

Referring to FIG. 12A, a fingerprint enrollment apparatus 1200 includesa sensor 1220, a processor 1210, and a memory 1230. The sensor 1220, theprocessor 1210, and the memory 1230 may communicate with one anotherthrough a bus 1240. Similarly, FIG. 12B illustrates the fingerprintenrollment apparatus 1200 including a user interface 1260, a display1270, and such a processor 1210 and memory 1230. Likewise, the bus 1240demonstrated in FIG. 12A may also be available for communication betweencomponents of the fingerprint enrollment apparatus 1200 of FIG. 12B.

The sensor 1220 illustrated in FIG. 12A may be a fingerprint sensor. Inaddition, the sensor 1220 may be included in, and represented by, eitheror both of the user interface 1260 and display 1270 illustrated in FIG.12B, e.g., depending on embodiment and implementation of the fingerprintsensor. For example, the user interface 1260 may include a fingerprintsensor with imaging sensor or display 1270 may be configured for bothoutward illumination and inward image capturing. Accordingly, belowreferences the sensor 1220 may equally apply to the configuration andoperations of the user interface 1260 and display 1270. Thus, forexample, the sensor 1220 may capture a fingerprint image using awell-known method, for example, a method of converting an optical imageto an electrical signal. The fingerprint image may be output to theprocessor 1210.

The processor 1210 may include one or more or all of the components ordevices described with reference to FIGS. 1 through 11, or perform oneor more or all of the processes and methods described with reference toFIGS. 1 through 11. The memory 1230 may store fingerprint images thatare captured by the sensor 1220 as well as such captured and enrolledfingerprints, and the memory 1240 may also store fingerprints enrolledfrom an external or remote device or alternative sensor or userinterface. Thus, according to one or more embodiments, the memory 1230may store an input fingerprint image captured by the sensor 1220, aresult of matching performed by the processor 1210, and/or a degree ofdiversity, a similarity, a similarity score, an enrollment score, and aneffective area size that are calculated by the processor 1210. Thememory 1230 may be a volatile memory or a nonvolatile memory.

The processor 1210 may execute processing instructions, such as in theform of a program, and control the fingerprint enrollment apparatus 1200accordingly. For example, such a program code to be executed by theprocessor 1210 may be stored in the memory 1230. The fingerprintenrollment apparatus 1200 may be representative of or connected to anexternal device, for example, a personal computer (PC) and a network,through the user interface 1260 of FIG. 12B as an input and outputdevice, and exchange data with the external device.

The fingerprint enrollment apparatus 1200 may include various electronicsystems, for example, mobile devices including, for example, a mobilephone, a smartphone, a personal digital assistant (PDA), a tabletcomputer, and a laptop computer, computing devices including, forexample, a PC, a tablet computer, and a netbook, and electronic productsincluding, for example, a television (TV), a smart TV, and a securitydevice for gate control. As only an example, FIG. 11 illustrates anexample where the fingerprint enrollment apparatus 1200 may be such amobile phone, smartphone, PDA, or tablet computer.

Although examples of methods of enrolling a fingerprint of a user usinga portion or an entirety of the fingerprint of the user are describedabove, such examples may be expanded to cases of enrolling a portion oran entirety of information on the fingerprint of the user and/or otherbiodata of the user. As only examples, the other biodata may include,for example, information on blood vessels of the user and information onan iris of the user. In such a case, the processor 1210 may receiveinput partial data corresponding to a portion of the biodata of the userfrom the sensor 1220, compare the input partial data to enrolled partialdata corresponding partial data of enrolled biodata, and enroll theinput partial data based on a result of the comparing.

For example, the sensor 1220 may include an image sensor configured torecognize a vein pattern of the user. The sensor 1220 may extract thevein pattern from skin of a dorsal side of a hand of the user. Thesensor 1220 may obtain an image including the vein pattern by maximizinga contrast of brightness of blood vessels of the user against the skinof the user using an infrared lighting and a filter. The sensor 1220 mayobtain a partial image corresponding to a portion of the vein pattern.In such an example, the processor 1210 may compare the partial imagecorresponding to the portion of the vein pattern to enrolled partialimages of the vein pattern, and enroll the obtained partial image.

For another example, the sensor 1220 may include a camera or iris sensorconfigured to recognize an iris pattern of the user. The sensor 1220 mayscan or capture the iris pattern between a pupil of the user and asclera, a white area of an eye, of the user. The sensor 1220 may obtaina partial image corresponding to a portion of the iris pattern. In suchan example, the processor 1210 may compare the partial imagecorresponding to the portion of the iris pattern to enrolled partialimages of the iris pattern, and enroll the obtained partial image.

The computing devices, mobile devices, fingerprint enrollment apparatus,fingerprint recognition apparatus, fingerprint enrollment apparatus1100, fingerprint enrollment apparatus 1200, fingerprint sensor 1110,database 1120, sensor 1220, processor 1210, memory 1230, bus 1240, userinterface 1260, and display 1270 in FIGS. 11-12B that perform theoperations described in this application are implemented by hardwarecomponents configured to perform the operations described in thisapplication that are performed by the hardware components. Examples ofhardware components that may be used to perform the operations describedin this application where appropriate include controllers, sensors,generators, drivers, memories, comparators, arithmetic logic units,adders, subtractors, multipliers, dividers, integrators, and any otherelectronic components configured to perform the operations described inthis application. In other examples, one or more of the hardwarecomponents that perform the operations described in this application areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer may be implemented byone or more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices that is configured to respond to andexecute instructions in a defined manner to achieve a desired result. Inone example, a processor or computer includes, or is connected to, oneor more memories storing instructions or software that are executed bythe processor or computer. Hardware components implemented by aprocessor or computer may execute instructions or software, such as anoperating system (OS) and one or more software applications that run onthe OS, to perform the operations described in this application. Thehardware components may also access, manipulate, process, create, andstore data in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described in this application, but inother examples multiple processors or computers may be used, or aprocessor or computer may include multiple processing elements, ormultiple types of processing elements, or both. For example, a singlehardware component or two or more hardware components may be implementedby a single processor, or two or more processors, or a processor and acontroller. One or more hardware components may be implemented by one ormore processors, or a processor and a controller, and one or more otherhardware components may be implemented by one or more other processors,or another processor and another controller. One or more processors, ora processor and a controller, may implement a single hardware component,or two or more hardware components. A hardware component may have anyone or more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1-11 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions in the specification, which disclosealgorithms for performing the operations that are performed by thehardware components and the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access memory (RAM), flashmemory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A processor implemented fingerprint enrollmentmethod comprising: performing a matching between a received inputfingerprint image of a user and one or more enrolled fingerprint imagesto identify a matched enrolled fingerprint image from the one or moreenrolled fingerprint images; extracting, corresponding to where theinput fingerprint and the matched enrolled fingerprint are determined tooverlap, a first shared region of the input fingerprint image and asecond shared region of the identified matched enrolled fingerprintimage overlapping the first shared region; and selectively, based on acalculated degree of diversity between the first shared region of theinput fingerprint image and the second shared region of the matchedenrolled fingerprint image, storing the input fingerprint as anotherenrolled fingerprint image.
 2. The method of claim 1, wherein theselective storing of the input fingerprint further comprises:calculating the degree of diversity in the fingerprint corresponding tothe first shared region and the second shared region; selectivelystoring the input fingerprint image as the other enrolled fingerprintimage based on the calculated degree of diversity.
 3. The method ofclaim 2, wherein the selective storing of the input fingerprint imageincludes selecting, dependent on the calculated degree of diversity,between a first of storing the input fingerprint image as the otherenrolled fingerprint image and a second of requesting the user to inputanother finger fingerprint image and not storing the input fingerprintimage as the other enrolled fingerprint image.
 4. The method of claim 3,wherein the selective storing of the input fingerprint image furtherincludes, dependent on a determined total number of stored enrolledfingerprint images and/or a calculated effective area size correspondingthe stored enrolled fingerprint images, not requesting the user to inputthe other fingerprint image and ceasing an enablement process of theenablement method.
 5. The method of claim 1, further comprisingcalculating the degree of diversity, including: calculating a similarityscore between the input fingerprint image and the matched enrolledfingerprint image based on a calculated similarity between the firstshared region and the second shared region, wherein, a relationshipbetween the similarity score and the degree of diversity exists suchthat, in response to an increase in the similarity score, the degree ofdiversity decreases, and in response to a decrease in the similarityscore, the degree of diversity increases.
 6. The method of claim 5,wherein the calculating of the similarity score comprises: calculatingthe similarity between the first shared region and the second sharedregion; and calculating the similarity score based on the calculatedsimilarity and a determined size of the first shared region or thesecond shared region.
 7. The method of claim 6, wherein the calculatingof the similarity score comprises: calculating the similarity based on anormalized cross correlation (NCC) or a phase correlation between theinput fingerprint image and the matched enrolled fingerprint image. 8.The method of claim 5, wherein the calculating of the similarity scorecomprises: calculating a shared area ratio based on a first ratiobetween a size of the first shared region or the second shared regionand a size of the input fingerprint image or based on a second ratiobetween the size of the first shared region or the second shared regionand a size of the matched enrolled fingerprint image; and calculatingthe similarity score based on the calculated shared area ratio and thecalculated similarity.
 9. The method of claim 8, wherein, in response tothe calculated similarity meeting a similarity threshold, the sharedarea ratio is the first ratio or the second ratio, in response to thesimilarity failing to meet the similarity threshold, the shared arearatio is a predefined value, and the similarity score is based on amultiplication of the shared area ratio and the calculated similarity.10. The method of claim 1, further comprising determining whether tocease a fingerprint enrollment process, including: calculating anenrollment score of plural enrolled fingerprint images, including theone or more enrolled fingerprint images and the stored other enrolledfingerprint image, based on a number of the plural enrolled fingerprintimages and similarity scores between the plural enrolled fingerprintimages; and determining whether to cease the fingerprint enrollmentprocess by comparing the enrollment score to a threshold score.
 11. Themethod of claim 10, wherein the calculating of the enrollment scorecomprises: calculating the enrollment score to be at least one SEnrollof:${S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\sum\limits_{j = {i + 1}}^{m}\; s_{ij}}} + {\sum\limits_{i = 1}^{m - 2}\; {\sum\limits_{j = {i + 1}}^{m - 1}\; {\sum\limits_{k = {j + 1}}^{m}\; {s_{ij}s_{jk}}}}}}};$${S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\max\limits_{i < j \leq m}s_{ij}}}}};$${S_{Enroll} = {\sum\limits_{i = 1}^{m}\; \left( {1 - {\max\limits_{{1 \leq j \leq m},{j \neq i}}s_{ij}}} \right)}};{or}$$S_{Enroll} = {\frac{m\left( {m - 1} \right)}{2} - {\sum\limits_{i = 1}^{m - 1}\; {\sum\limits_{j = {i + 1}}^{m}\; {s_{ij}.}}}}$wherein m denotes the number of the plural enrolled fingerprint images,and Sij denotes a similarity score between an i-th enrolled fingerprintimage and a j-th enrolled fingerprint image of the plural enrolledfingerprint images.
 12. The method of claim 1, further comprisingdetermining whether to cease a fingerprint enrollment process,including: updating respective degrees of diversity corresponding torespective overlapping regions, with respect to the input fingerprintimage, of plural enrolled fingerprint images, including the one or moreenrolled fingerprint images and the stored other enrolled fingerprintimage, based on the calculated degree of diversity; calculating aneffective area size of the plural enrolled fingerprint images based onthe updated degrees of diversity; and determining whether to cease thefingerprint enrollment process by comparing the calculated effectivearea size to a threshold area size.
 13. The method of claim 12, whereina relationship between a size of the respective overlapping regions andthe effective area size exists such that, in response to an increase insize of the respective overlapping regions of the plural enrolledfingerprint images, the effective area size decreases, and wherein arelationship between the updated respective degrees of diversity and theeffective area size exists such that, in response to an increase in theupdated degrees of diversity, the effective area size increases.
 14. Themethod of claim 1, further comprising calculating the degree ofdiversity, where a result of the calculating of the degree of diversityis dependent on at least one of a direction of a fingerprintcorresponding to the input fingerprint image, a direction, adistribution, and a magnitude of a finger pressure corresponding to theinput fingerprint image, and a humidity and/or other condition of aportion of skin corresponding to the input fingerprint image.
 15. Themethod of claim 1, wherein the matching comprises: obtaining at leastone phase correlation between the input fingerprint image and the one ormore enrolled fingerprint images based on a frequency-based matchingmethod; obtaining at least one of a translation, a rotation, or a scalebetween the input fingerprint image and the one or more enrolledfingerprint images based on the obtained phase correlation; anddetermining whether, and which of, any of the one or more enrolledfingerprint images match the input fingerprint image based on at leastone of the obtained translation, the obtained rotation, or the obtainedscale.
 16. The method of claim 1, further comprising performing arecognition process to compare the input fingerprint image or anotherinput fingerprint image to at least the one or more enrolled fingerprintimages and selectively enable user access to stored information of acomputing device that performs a fingerprint enrollment processincluding the selective storing the input fingerprint image as anotherenrolled fingerprint image.
 17. A processor implemented fingerprintenrollment method comprising: comparing an input fingerprint image of auser to multiple enrolled fingerprint images, to recognize the inputfingerprint image as having matched correspondence to an enrolledfingerprint of the user based on a result of the comparing; measuringrespective degrees of diversity corresponding to one or more overlappingregions between the recognized input fingerprint image and one ormatched enrolled fingerprint images of the multiple enrolled fingerprintimages; and determining whether to enroll the input fingerprint imagebased on the measured respective degrees of diversity, wherein therespective degrees of diversity are calculated by calculating respectivesimilarities between a first shared region of the input fingerprintimage corresponding to the one or more overlapping regions andrespective second shared regions of the one or more matched enrolledfingerprint images corresponding to the one or more overlapping regions.18. A non-transitory computer-readable storage medium storinginstructions, that when executed by a processor, cause the processor toperform the method of claim
 1. 19. A fingerprint enrollment apparatuscomprising: a fingerprint sensor configured to receive an inputfingerprint image of a user; and a processor configured to perform amatching of the input fingerprint image to one or more enrolledfingerprint images to identify a matched enrolled fingerprint image,extract, corresponding to where the input fingerprint and the matchedenrolled fingerprint are determined to overlap, a first shared region ofthe input fingerprint image and a second shared region of the identifiedmatched enrolled fingerprint image overlapping the first shared region,calculate a degree of diversity between the first shared region and thesecond shared region, and determine whether to store the inputfingerprint image as another enrolled fingerprint image based on thecalculated degree of diversity.
 20. The fingerprint enrollment apparatusof claim 19, wherein the processor is configured to calculate asimilarity score between the input fingerprint image and the matchedenrolled fingerprint image based on a calculated similarity between thefirst shared region and the second shared region, wherein, arelationship between the similarity score and the degree of diversityexists such that, in response to an increase in the similarity score,the degree of diversity decreases, and in response to a decrease in thesimilarity score, the degree of diversity increases.
 21. The fingerprintenrollment apparatus of claim 20, wherein the processor is configured tocalculate the similarity between the first shared region and the secondshared region, and calculate the similarity score based on thecalculated similarity and a determined size of the first shared regionor the second shared region.
 22. The fingerprint enrollment apparatus ofclaim 20, wherein the processor is configured to calculate a shared arearatio based on a first ratio between a size of the first shared regionor the second shared region and a size of the input fingerprint image orbased on a second ratio between the size of the first shared region orthe second shared region and a size of the matched enrolled fingerprintimage, and calculate the similarity score based on the calculated sharedarea ratio and the similarity.
 23. The fingerprint enrollment apparatusof claim 20, wherein the processor is configured to calculate anenrollment score of plural enrolled fingerprint images, including theone or more enrolled fingerprint images and the stored other enrolledfingerprint image, based on a number of the plural enrolled fingerprintimages and similarity scores of the plural enrolled fingerprint images,and determine whether to cease a fingerprint enrollment process bycomparing the calculated enrollment score to a threshold score.
 24. Thefingerprint enrollment apparatus of claim 19, wherein the processor isconfigured to update respective degrees of diversity corresponding torespective overlapping regions, with respect to the input fingerprintimage, of plural enrolled fingerprint images, including the one or moreenrolled fingerprint images and the stored other enrolled fingerprintimage, based on the calculated degree of diversity, calculate aneffective area size of the plural enrolled fingerprint images based onthe updated respective degrees of diversity, and determine whether tocease a fingerprint enrollment process by comparing the calculatedeffective area size to a threshold area size.
 25. The fingerprintenrollment apparatus of claim 19, wherein the processor is furtherconfigured to perform a recognition process to compare the inputfingerprint image or another input fingerprint image to at least the oneor more enrolled fingerprint images to selectively enable user access tostored information of the enrollment apparatus.
 26. The fingerprintenrollment apparatus of claim 25, further comprising another fingerprintsensor, wherein the processor is further configured to perform therecognition process to compare the other input fingerprint imagecaptured by the other fingerprint sensor to at least the one or moreenrolled fingerprint images and selectively enable user access to storedinformation of the enrollment apparatus based on a result of therecognition process, wherein the other fingerprint sensor has adifferent image capturing configuration than the fingerprint sensor, soas to capture a different fingerprint image shape or size than capturedby the fingerprint sensor.
 27. An enrollment apparatus comprising: aprocessor configured to perform a matching of an input bio-image of auser to one or more enrolled bio-images to identify a matched enrolledbio-image, extract, corresponding to where the input bio-image and thematched enrolled bio-image are determined to overlap, a first sharedregion of the input bio-image and a second shared region of theidentified matched enrolled bio-image overlapping the first sharedregion, calculate a degree of diversity between the first shared regionand the second shared region, and determine whether to store the inputbio-image as another enrolled bio-image image based on the calculateddegree of diversity.
 28. The enrollment apparatus of claim 27, furthercomprising a bio-sensor configured to receive the input bio-image of theuser.
 29. The enrollment apparatus of claim 28, wherein the bio-sensoris a fingerprint sensor configured to capture only a portion of acorresponding fingerprint corresponding to a sensor area of thefingerprint sensor, with the input bio-image being a corresponding inputfingerprint image of only the portion of the corresponding fingerprint.